Exploring the impact of the New York State repeal of nonmedical vaccination exemptions on student enrollment, absenteeism, and school workload: Perspectives from a survey of school administrators
Bibliographic record
Abstract
In June 2019, New York State (NYS) adopted Senate Bill 2994A eliminating nonmedical vaccine exemptions from school entry laws. Since student noncompliance with the law required school exclusion, we sought to evaluate the law's effects on student enrollment and absenteeism, and school workloads related to its implementation. In November 2019, we sent an electronic survey to NYS (excluding New York City) schools. Due to the COVID-19 pandemic, outreach was curtailed in March 2020 with 525 (14%) of 3,759 eligible schools responding. To account for non-response, results were analyzed using inverse probability weighting. After weighting, 39% (95% CI: 34%, 44%) of schools reported enrollment changes and 31% (95% CI: 26%, 36%) of schools reported absenteeism related to the law. In addition, 95% (95% CI: 93%, 98%) of schools reported holding meetings and/or preparing correspondence about the law, spending a mean of 14 (95% CI: 11, 18) hours on these communication efforts. Schools in the highest pre-mandate nonmedical exemption tertile (vs. lowest) were more likely to report enrollment and absenteeism changes, and higher workloads. While our results should be interpreted with caution, changes in student enrollment, absenteeism, and school workloads may represent important considerations for policymakers planning similar legislation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".